Statistical and geostatistical analysis of wind: A case study of direction statistics

To study the applicability of geostatistics for vector data, wind velocity data given by AMeDAS (Automated Meteorological Data Acquisition System) have been analyzed statistically and geostatistically. The study area consists of two districts, the mountainous Chubu and plain Kanto districts in central Japan. Exponential function illustrates well statistical distribution of wind speeds in both districts. Temporal experimental variograms of wind speeds, directions and velocities suggest daily duration, and wind is stronger by day than by night. Some spatial experimental variogram of wind speeds are traditional spherical schemes showing clear nugget effects, sills and ranges whose values vary 50–130 km, while other variograms are not typical schemes but show flat or gradually increasing curves. If variograms of momentary wind speeds have no range, an empirical variograms of temporally averaged wind speeds does not also show a range. The spatial experimental variograms have no range suggest that the present observation system of AMeDAS is too low in station density for the assessment of wind power generation.     

Statistical significance of the LMS regression

We propose the use of simulation in order to obtain a statistical significance measure of the least median of squares (LMS) regression coefficients. We shuffle the values of the dependent variable many times (e.g. 100), so as to preserve their distribution, and we calculate the LMS regression coefficients for every shuffled data. In this way we form a confidence interval for the slope centered on 0, because the slopes of the shuffled data are considered statistically equal to 0. The coefficients of the original data are considered significant if they are not belong on the above mentioned interval.     

Computer-mediated persuasion in online reviews: Statistical versus narrative evidence

The current study examined the effects of online product reviews on individuals’ credibility perceptions of the reviews and their attitudes about the product targeted in the reviews. Study 1 used a 2 (review type: statistical and narrative) × 2 (review valence: negative and positive) design, and study 2 used a 2 (statistical review valence: positive and negative) × 2 (narrative review valence: positive and negative) design. The findings of study 1 were consistent with those of study 2 and indicated that negative statistical reviews were perceived as more credible than negative narrative reviews, while the credibility of positive statistical reviews did not differ from that of positive narrative reviews. On the other hand, statistical reviews and narrative reviews did not differ in terms of affecting individuals’ attitudes about the product. The current study also found that the vividness and valence of each review type had varying effects on review credibility and attitudes about the product. The implications of these and other findings are discussed in detail in the paper.     

Statistical and fuzzy optimal decisions: I. Statistical decisions

In the proposed series of two articles, the methods of optimal statistical and fuzzy (possibility-theoretical) decisions are described from a unified point of view. In the second article [22], we consider the possibility-theoretical methods for optimal decisions (to be more precise, optimal identification methods or, in other words, methods for optimal choice of one of a number of alternative hypotheses concerning the affiliation of the object of investigation to one of a finite number of classes), as well as the methods for optimal estimation of fuzzy elements. The quality of the decision is determined in terms of the risk of losses accompanying the decision and is characterized by the values of possibility and/or inevitability of losses. The presentation follows the scheme adopted in the theory of statistical decisions [16, 17]. In the present work, which is the first in the series, elements of statistical decision theory, whose analogs were obtained in [22] and which make it possible to reconstruct the possibility empirically, are presented for the convenience of the readers who want to trace the analogy between the probability-theoretical methods and the possibility-theoretical methods for decision optimization [22]. Yurii P. Pyt’ev. Graduated from the Faculty of Physics, Moscow State University. Professor, Head of the Department of Computer Methods in Physics, Faculty of Physics, Moscow State University. Scientific Interests: mathematical methods of data analysis and interpretation, mathematical modeling of measuring-computing systems of superhigh resolution, and fuzzy and uncertain fuzzy mathematics. Author of more than 300 publications on mathematics, mathematical physics, and information science, including several monographs.     

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these “less thans” is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data.We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards.     

Classification of Mediterranean crops with multisensor data: per-pixel versus per-object statistics and image segmentation

Remote sensing of land cover in Mediterranean regions is complicated by the high landscape diversity, which is typical of both natural and agricultural lands. This spatial complexity reduces the accuracy of the common per-pixel classification of multi-spectral remotely sensed imagery. In this paper we show that per-field statistics derived from multi-spectral imagery enhances separability between different crops and terrain categories. We also found that Synthetic Aperture Radar (SAR) imagery processed with Thematic Mapper (TM) imagery in a synergistic context produces a higher enhancement of discrimination if per-field statistics are used. Finally, we show that image segmentation is a convenient way to apply this approach avoiding field digitizing by computing per-segment statistics of training fields and classifying the segmented image through Linear Canonical Discriminant Analysis.     

Statistical and fuzzy optimal decisions: II. Fuzzy decisions

In the paper, methods for optimal possibility-theoretical decisions are considered on examples of fuzzy identification and fuzzy estimation. The quality of the decision is determined in terms of the risk of losses accompanying the decision and is characterized by the values of possibility and/or inevitability of losses. Yurii P. Pyt’ev. Graduated from the Faculty of Physics, Moscow State University. Professor, Head of the Department of Computer Methods in Physics, Faculty of Physics, Moscow State University. Scientific interests: mathematical methods of data analysis and interpretation, mathematical modeling of measuring-computing systems of superhigh resolution, and fuzzy and uncertain fuzzy mathematics. Author of more than 300 publications on mathematics, mathematical physics, and information science, including several monographs.     

Sorting of neural spikes: When wavelet based methods outperform principal component analysis

Sorting of the extracellularly recorded spikes is a basic prerequisite for analysis of the cooperative neural behavior and neural code. Fundamentally the sorting performance is defined by the quality of discriminative features extracted from spike waveforms. Here we discuss two features extraction approaches: principal component analysis (PCA), and wavelet transform (WT). We show that only when properly tuned to the data, the WT technique may outperform PCA. We present a novel method for extraction of spike features based on a combination of PCA and continuous WT. The method automatically tunes its WT part to the data structure making use of knowledge obtained by PCA. We demonstrate the method on simulated and experimental data sets.     

Convergence analysis of SART: optimization and statistics

Simultaneous algebraic reconstruction technique (SART) [A. Andersen, Algebraic reconstruction in CT from limited views, IEEE Trans. Med. Imaging 8 (1989), pp. 50–55; A. Andersen and A. Kak, Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm, Ultrason. Imaging 6 (1984), pp. 81–94] is an iterative method for solving inverse problems of form Ax(+n)=b. These kinds of problems arise, for example, in computed tomography (CT) reconstruction, in which case A is obtained from the discrete Radon transform. In this paper, we provide several methods for derivation of SART and connections between SART and other methods. Using these connections, we also prove the convergence of SART in different ways. These approaches are from optimization and statistical points of view and can be applied to other Landweber-like schemes such as Cimmino's algorithm and component averaging. Furthermore, the noisy case is considered and the error estimation is given. Several numerical experiments for CT reconstruction are provided to demonstrate the convergence results in practice.     

Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark

In this paper, a data-based covariance benchmark is proposed for control performance monitoring. Within the covariance monitoring scheme, generalized eigenvalue analysis is used to extract the directions with the degraded or improved control performance against the benchmark. It is shown that the generalized eigenvalues and the covariance-based performance index are invariant to scaling of the data. A statistical inference method is further developed for the generalized eigenvalues and the corresponding confidence intervals are derived from asymptotic statistics. This procedure can be used to determine the directions or subspaces with significantly worse or better performance versus the benchmark. The covariance-based performance indices within the isolated worse and better performance subspaces are then derived to assess the performance degradation and improvement. Two simulated examples, a multiloop control and a multivariable MPC system, are provided to illustrate the utility of the proposed approach. Then an industrial wood waste burning power boiler unit is used to demonstrate the effectiveness of the method.     

Detecting independent motion: the statistics of temporal continuity

We consider a problem central in aerial visual surveillance applications; detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames, and the definition of a constraint violation function which acts as an indicator of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filter. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence     

基于多元统计分析的《计算机应用基础》考试成绩分析

在对计算机考试成绩进行初步分析的基础上,针对计算机课程的特点,对学生在各个模块掌握的均衡性进行了分析,总结了教学过程中存在的问题,并提出了改进的教学策略,为今后的教学改革提供参考。     

‘An ocean where each kind. . .’: Statistical analysis and some major determinants of literary style

The statistical analysis of literary texts has yielded valuable results, not least when it has treated of the frequency patterns of very common words. But, whereas particular frequency patterns have usually been examined as discrete phenomena, it is possible to correlate the frequency profiles of all the very common words, to subject the resulting correlation matrix to eigen analysis, and to present the results in graphic form. The specimens offered here deal, first, with differences among Jane Austen's characters and, secondly, with differences between authors. The most striking general differences among the authors studied relate to historical eras and authorial gender.     

ISMOD: an all-subsets regression program for generalized linear models. I. Statistical and computational background

This paper describes a system written to carry out regression analyses under certain generalized linear models that are widely used in biomedical research. These include continuous response models such as the Weibull, log-logistic, log-normal and Cox proportional hazards models used in survival analysis, and also discrete Poisson, binomial and multinomial response regression models. The system fits models, generates residuals and other diagnostic output, and has an all-subsets regression feature. This paper describes the models implemented and gives statistical background; Part II describes the ISMOD system and presents examples of its application.     

Fuzzy versus quantitative association rules: a fair data-driven comparison.

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.     

Statistical Analysis of Multiplicative Schemes: Basic Distributions

New distributions used for statistical processing of multiplicative schemes are introduced and examined in the paper. Some new characteristics of random quantities (geometrical expectation, relative variance and variation, etc.) are studied.